This scoping review systematically maps peer-reviewed evidence on AI-based digital technologies used for workplace health promotion and performance management among healthcare workers, published between 2015 and July 2025. The authors argue that AI tools — spanning machine learning, natural language processing, chatbots, wearables, and predictive analytics — show measurable promise across two primary domains: AI-enabled health monitoring and intervention, and AI-driven performance optimization. From an initial pool of 351 records, 21 studies met full eligibility criteria under the PRISMA-ScR and PCC frameworks. Reported individual-level benefits included reductions in stress, burnout, anxiety, and musculoskeletal pain, while organizational-level benefits included improvements in workflow efficiency, documentation quality, leadership support, and staff engagement. However, the review explicitly identifies significant limitations: short study durations, methodological heterogeneity across included studies, privacy and ethical concerns, and variable rates of staff adoption. The authors conclude that while AI-based tools hold potential for enhancing both worker health and organizational sustainability in healthcare settings, long-term effectiveness remains unestablished, and rigorous, human-centered, and privacy-conscious research designs are needed before broader conclusions can be drawn. Key insights: Two dominant application domains emerged across 21 included studies: AI-enabled health monitoring and intervention (targeting stress, burnout, mental health, and musculoskeletal outcomes) and AI-driven performance optimization (targeting workflow efficiency, documentation, and operational metrics). Despite broadly positive reported outcomes, the evidence base is characterized by short study durations, heterogeneous methodologies, and inconsistent outcome measurement tools, making cross-study comparisons and generalization unreliable. Privacy, data security, lack of transparency, and ethical concerns consistently appeared as adoption barriers, while personalization capability, efficiency gains, and improved accuracy were identified as key enablers of AI tool uptake among healthcare workers. Practical takeaways: Organizations deploying AI-based health promotion or performance management tools in healthcare settings encounter variable adoption rates, suggesting that trust-building, staff education, and governance structures are material factors in implementation outcomes. The intersection of individual wellbeing outcomes and organizational performance metrics in AI-enabled systems points toward an emerging integrated model of workforce management in healthcare, though the evidence for long-term impact across both dimensions remains limited.